





import gc
import unittest

import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
    ClapTextConfig,
    ClapTextModelWithProjection,
    RobertaTokenizer,
    SpeechTaHifiGan,
    SpeechTaHifiGanConfig,
)

from diffusers import (
    AudioLDMPipeline,
    AutoencoderKL,
    DDIMScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from ..pipeline_params import TEXT_TO_AUDIO_BATCH_PARAMS, TEXT_TO_AUDIO_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class    __lowerCAmelCase			(     _a,	unittest.TestCase	):
			lowerCamelCase_	:	Dict			     = AudioLDMPipeline
			lowerCamelCase_	:	List[Any]			     = TEXT_TO_AUDIO_PARAMS
			lowerCamelCase_	:	Optional[int]			     = TEXT_TO_AUDIO_BATCH_PARAMS
			lowerCamelCase_	:	Any			     = frozenset(
			    [
			        '''num_inference_steps''',
			        '''num_waveforms_per_prompt''',
			        '''generator''',
			        '''latents''',
			        '''output_type''',
			        '''return_dict''',
			        '''callback''',
			        '''callback_steps''',
			    ]	)






			def      lowerCamelCase    (self       )	->  Union[str, Any]:


							'''simple docstring'''



							torch.manual_seed(0       )
							snake_case_		:       int           =    UNetaDConditionModel(
							    block_out_channels=(32, 64)   ,				layers_per_block=2   ,				sample_size=32   ,				in_channels=4   ,				out_channels=4   ,				down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')   ,				up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')   ,				cross_attention_dim=(32, 64)   ,				class_embed_type='''simple_projection'''   ,				projection_class_embeddings_input_dim=32   ,				class_embeddings_concat=__magic_name__   ,				)
							snake_case_		:       Any           =    DDIMScheduler(
							    beta_start=0.00_085   ,				beta_end=0.012   ,				beta_schedule='''scaled_linear'''   ,				clip_sample=__magic_name__   ,				set_alpha_to_one=__magic_name__   ,				)
							torch.manual_seed(0       )
							snake_case_		:       List[Any]           =    AutoencoderKL(
							    block_out_channels=[32, 64]   ,				in_channels=1   ,				out_channels=1   ,				down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']   ,				up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']   ,				latent_channels=4   ,				)
							torch.manual_seed(0       )
							snake_case_		:       Any           =    ClapTextConfig(
							    bos_token_id=0   ,				eos_token_id=2   ,				hidden_size=32   ,				intermediate_size=37   ,				layer_norm_eps=1e-05   ,				num_attention_heads=4   ,				num_hidden_layers=5   ,				pad_token_id=1   ,				vocab_size=1000   ,				projection_dim=32   ,				)
							snake_case_		:       str           =    ClapTextModelWithProjection(__magic_name__       )
							snake_case_		:       Union[str, Any]           =    RobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-roberta'''   ,				model_max_length=77       )

							snake_case_		:       Optional[int]           =    SpeechTaHifiGanConfig(
							    model_in_dim=8   ,				sampling_rate=1_6000   ,				upsample_initial_channel=16   ,				upsample_rates=[2, 2]   ,				upsample_kernel_sizes=[4, 4]   ,				resblock_kernel_sizes=[3, 7]   ,				resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5]]   ,				normalize_before=__magic_name__   ,				)

							snake_case_		:       List[str]           =    SpeechTaHifiGan(__magic_name__       )

							snake_case_		:       Dict           =    {
							    '''unet''': unet,
							    '''scheduler''': scheduler,
							    '''vae''': vae,
							    '''text_encoder''': text_encoder,
							    '''tokenizer''': tokenizer,
							    '''vocoder''': vocoder,
							}
							return components






			def      lowerCamelCase    (self   ,				__magic_name__   ,				__magic_name__=0       )	->  Optional[Any]:


							'''simple docstring'''



							if str(__magic_name__       ).startswith('''mps'''       ):
											snake_case_		:       Tuple           =    torch.manual_seed(__magic_name__       )
							else:
											snake_case_		:       Union[str, Any]           =    torch.Generator(device=__magic_name__       ).manual_seed(__magic_name__       )
							snake_case_		:       Optional[int]           =    {
							    '''prompt''': '''A hammer hitting a wooden surface''',
							    '''generator''': generator,
							    '''num_inference_steps''': 2,
							    '''guidance_scale''': 6.0,
							}
							return inputs






			def      lowerCamelCase    (self       )	->  Union[str, Any]:


							'''simple docstring'''



							snake_case_		:       Optional[Any]           =    '''cpu'''  # ensure determinism for the device-dependent torch.Generator

							snake_case_		:       Dict           =    self.get_dummy_components()
							snake_case_		:       List[str]           =    AudioLDMPipeline(**__magic_name__       )
							snake_case_		:       str           =    audioldm_pipe.to(__magic_name__       )
							audioldm_pipe.set_progress_bar_config(disable=__magic_name__       )

							snake_case_		:       int           =    self.get_dummy_inputs(__magic_name__       )
							snake_case_		:       Tuple           =    audioldm_pipe(**__magic_name__       )
							snake_case_		:       int           =    output.audios[0]

							assert audio.ndim == 1
							assert len(__magic_name__       ) == 256

							snake_case_		:       Optional[Any]           =    audio[:10]
							snake_case_		:       Tuple           =    np.array(
							    [-0.0_050, 0.0_050, -0.0_060, 0.0_033, -0.0_026, 0.0_033, -0.0_027, 0.0_033, -0.0_028, 0.0_033]       )

							assert np.abs(audio_slice - expected_slice       ).max() < 1e-2






			def      lowerCamelCase    (self       )	->  Union[str, Any]:


							'''simple docstring'''



							snake_case_		:       Optional[Any]           =    self.get_dummy_components()
							snake_case_		:       List[Any]           =    AudioLDMPipeline(**__magic_name__       )
							snake_case_		:       int           =    audioldm_pipe.to(__magic_name__       )
							snake_case_		:       List[Any]           =    audioldm_pipe.to(__magic_name__       )
							audioldm_pipe.set_progress_bar_config(disable=__magic_name__       )

							snake_case_		:       Any           =    self.get_dummy_inputs(__magic_name__       )
							snake_case_		:       Optional[Any]           =    3 * [inputs['''prompt''']]

							# forward
							snake_case_		:       str           =    audioldm_pipe(**__magic_name__       )
							snake_case_		:       Optional[Any]           =    output.audios[0]

							snake_case_		:       str           =    self.get_dummy_inputs(__magic_name__       )
							snake_case_		:       List[Any]           =    3 * [inputs.pop('''prompt'''       )]

							snake_case_		:       int           =    audioldm_pipe.tokenizer(
							    __magic_name__   ,				padding='''max_length'''   ,				max_length=audioldm_pipe.tokenizer.model_max_length   ,				truncation=__magic_name__   ,				return_tensors='''pt'''   ,				)
							snake_case_		:       Tuple           =    text_inputs['''input_ids'''].to(__magic_name__       )

							snake_case_		:       str           =    audioldm_pipe.text_encoder(
							    __magic_name__   ,				)
							snake_case_		:       List[Any]           =    prompt_embeds.text_embeds
							# additional L_2 normalization over each hidden-state
							snake_case_		:       Tuple           =    F.normalize(__magic_name__   ,				dim=-1       )

							snake_case_		:       List[Any]           =    prompt_embeds

							# forward
							snake_case_		:       Optional[Any]           =    audioldm_pipe(**__magic_name__       )
							snake_case_		:       Tuple           =    output.audios[0]

							assert np.abs(audio_a - audio_a       ).max() < 1e-2






			def      lowerCamelCase    (self       )	->  str:


							'''simple docstring'''



							snake_case_		:       Tuple           =    self.get_dummy_components()
							snake_case_		:       List[Any]           =    AudioLDMPipeline(**__magic_name__       )
							snake_case_		:       Union[str, Any]           =    audioldm_pipe.to(__magic_name__       )
							snake_case_		:       List[Any]           =    audioldm_pipe.to(__magic_name__       )
							audioldm_pipe.set_progress_bar_config(disable=__magic_name__       )

							snake_case_		:       Optional[Any]           =    self.get_dummy_inputs(__magic_name__       )
							snake_case_		:       Tuple           =    3 * ['''this is a negative prompt''']
							snake_case_		:       Union[str, Any]           =    negative_prompt
							snake_case_		:       Optional[int]           =    3 * [inputs['''prompt''']]

							# forward
							snake_case_		:       str           =    audioldm_pipe(**__magic_name__       )
							snake_case_		:       str           =    output.audios[0]

							snake_case_		:       List[Any]           =    self.get_dummy_inputs(__magic_name__       )
							snake_case_		:       Dict           =    3 * [inputs.pop('''prompt'''       )]

							snake_case_		:       List[str]           =    []
							for p in [prompt, negative_prompt]:
											snake_case_		:       List[str]           =    audioldm_pipe.tokenizer(
											    __magic_name__   ,				padding='''max_length'''   ,				max_length=audioldm_pipe.tokenizer.model_max_length   ,				truncation=__magic_name__   ,				return_tensors='''pt'''   ,				)
											snake_case_		:       Optional[Any]           =    text_inputs['''input_ids'''].to(__magic_name__       )

											snake_case_		:       int           =    audioldm_pipe.text_encoder(
											    __magic_name__   ,				)
											snake_case_		:       str           =    text_embeds.text_embeds
											# additional L_2 normalization over each hidden-state
											snake_case_		:       List[Any]           =    F.normalize(__magic_name__   ,				dim=-1       )

											embeds.append(__magic_name__       )

							snake_case_     ,				snake_case_		:       Optional[int]           =    embeds

							# forward
							snake_case_		:       List[Any]           =    audioldm_pipe(**__magic_name__       )
							snake_case_		:       Optional[int]           =    output.audios[0]

							assert np.abs(audio_a - audio_a       ).max() < 1e-2






			def      lowerCamelCase    (self       )	->  Optional[Any]:


							'''simple docstring'''



							snake_case_		:       List[Any]           =    '''cpu'''  # ensure determinism for the device-dependent torch.Generator
							snake_case_		:       Optional[Any]           =    self.get_dummy_components()
							snake_case_		:       int           =    PNDMScheduler(skip_prk_steps=__magic_name__       )
							snake_case_		:       Optional[int]           =    AudioLDMPipeline(**__magic_name__       )
							snake_case_		:       List[str]           =    audioldm_pipe.to(__magic_name__       )
							audioldm_pipe.set_progress_bar_config(disable=__magic_name__       )

							snake_case_		:       int           =    self.get_dummy_inputs(__magic_name__       )
							snake_case_		:       Union[str, Any]           =    '''egg cracking'''
							snake_case_		:       Union[str, Any]           =    audioldm_pipe(**__magic_name__   ,				negative_prompt=__magic_name__       )
							snake_case_		:       Any           =    output.audios[0]

							assert audio.ndim == 1
							assert len(__magic_name__       ) == 256

							snake_case_		:       int           =    audio[:10]
							snake_case_		:       Any           =    np.array(
							    [-0.0_051, 0.0_050, -0.0_060, 0.0_034, -0.0_026, 0.0_033, -0.0_027, 0.0_033, -0.0_028, 0.0_032]       )

							assert np.abs(audio_slice - expected_slice       ).max() < 1e-2






			def      lowerCamelCase    (self       )	->  int:


							'''simple docstring'''



							snake_case_		:       List[Any]           =    '''cpu'''  # ensure determinism for the device-dependent torch.Generator
							snake_case_		:       Dict           =    self.get_dummy_components()
							snake_case_		:       int           =    PNDMScheduler(skip_prk_steps=__magic_name__       )
							snake_case_		:       Dict           =    AudioLDMPipeline(**__magic_name__       )
							snake_case_		:       Tuple           =    audioldm_pipe.to(__magic_name__       )
							audioldm_pipe.set_progress_bar_config(disable=__magic_name__       )

							snake_case_		:       str           =    '''A hammer hitting a wooden surface'''

							# test num_waveforms_per_prompt=1 (default)
							snake_case_		:       int           =    audioldm_pipe(__magic_name__   ,				num_inference_steps=2       ).audios

							assert audios.shape == (1, 256)

							# test num_waveforms_per_prompt=1 (default) for batch of prompts
							snake_case_		:       Optional[int]           =    2
							snake_case_		:       Dict           =    audioldm_pipe([prompt] * batch_size   ,				num_inference_steps=2       ).audios

							assert audios.shape == (batch_size, 256)

							# test num_waveforms_per_prompt for single prompt
							snake_case_		:       int           =    2
							snake_case_		:       Optional[Any]           =    audioldm_pipe(__magic_name__   ,				num_inference_steps=2   ,				num_waveforms_per_prompt=__magic_name__       ).audios

							assert audios.shape == (num_waveforms_per_prompt, 256)

							# test num_waveforms_per_prompt for batch of prompts
							snake_case_		:       Any           =    2
							snake_case_		:       Tuple           =    audioldm_pipe(
							    [prompt] * batch_size   ,				num_inference_steps=2   ,				num_waveforms_per_prompt=__magic_name__       ).audios

							assert audios.shape == (batch_size * num_waveforms_per_prompt, 256)






			def      lowerCamelCase    (self       )	->  Optional[int]:


							'''simple docstring'''



							snake_case_		:       Tuple           =    '''cpu'''  # ensure determinism for the device-dependent torch.Generator
							snake_case_		:       List[str]           =    self.get_dummy_components()
							snake_case_		:       Dict           =    AudioLDMPipeline(**__magic_name__       )
							snake_case_		:       Tuple           =    audioldm_pipe.to(__magic_name__       )
							audioldm_pipe.set_progress_bar_config(disable=__magic_name__       )
							snake_case_		:       List[Any]           =    audioldm_pipe.vocoder.config.sampling_rate

							snake_case_		:       int           =    self.get_dummy_inputs(__magic_name__       )
							snake_case_		:       str           =    audioldm_pipe(audio_length_in_s=0.016   ,				**__magic_name__       )
							snake_case_		:       List[str]           =    output.audios[0]

							assert audio.ndim == 1
							assert len(__magic_name__       ) / vocoder_sampling_rate == 0.016

							snake_case_		:       Optional[Any]           =    audioldm_pipe(audio_length_in_s=0.032   ,				**__magic_name__       )
							snake_case_		:       Any           =    output.audios[0]

							assert audio.ndim == 1
							assert len(__magic_name__       ) / vocoder_sampling_rate == 0.032






			def      lowerCamelCase    (self       )	->  Dict:


							'''simple docstring'''



							snake_case_		:       List[str]           =    self.get_dummy_components()
							snake_case_		:       Union[str, Any]           =    AudioLDMPipeline(**__magic_name__       )
							snake_case_		:       Optional[Any]           =    audioldm_pipe.to(__magic_name__       )
							audioldm_pipe.set_progress_bar_config(disable=__magic_name__       )

							snake_case_		:       int           =    ['''hey''']

							snake_case_		:       str           =    audioldm_pipe(__magic_name__   ,				num_inference_steps=1       )
							snake_case_		:       int           =    output.audios.shape
							assert audio_shape == (1, 256)

							snake_case_		:       Optional[Any]           =    audioldm_pipe.vocoder.config
							config.model_in_dim *= 2
							snake_case_		:       str           =    SpeechTaHifiGan(__magic_name__       ).to(__magic_name__       )
							snake_case_		:       Union[str, Any]           =    audioldm_pipe(__magic_name__   ,				num_inference_steps=1       )
							snake_case_		:       Any           =    output.audios.shape
							# waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
							assert audio_shape == (1, 256)






			def      lowerCamelCase    (self       )	->  Dict:


							'''simple docstring'''



							self._test_attention_slicing_forward_pass(test_mean_pixel_difference=__magic_name__       )






			def      lowerCamelCase    (self       )	->  List[str]:


							'''simple docstring'''



							self._test_inference_batch_single_identical(test_mean_pixel_difference=__magic_name__       )






			@unittest.skipIf(
			    torch_device != '''cuda''' or not is_xformers_available()   ,				reason='''XFormers attention is only available with CUDA and `xformers` installed'''   ,				)
			def      lowerCamelCase    (self       )	->  List[Any]:


							'''simple docstring'''



							self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=__magic_name__       )




@slow
class    __lowerCAmelCase			(     unittest.TestCase	):






			def      lowerCamelCase    (self       )	->  Optional[int]:


							'''simple docstring'''



							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()






			def      lowerCamelCase    (self   ,				__magic_name__   ,				__magic_name__="cpu"   ,				__magic_name__=torch.floataa   ,				__magic_name__=0       )	->  Dict:


							'''simple docstring'''



							snake_case_		:       int           =    torch.Generator(device=__magic_name__       ).manual_seed(__magic_name__       )
							snake_case_		:       Optional[int]           =    np.random.RandomState(__magic_name__       ).standard_normal((1, 8, 128, 16)       )
							snake_case_		:       Optional[Any]           =    torch.from_numpy(__magic_name__       ).to(device=__magic_name__   ,				dtype=__magic_name__       )
							snake_case_		:       Dict           =    {
							    '''prompt''': '''A hammer hitting a wooden surface''',
							    '''latents''': latents,
							    '''generator''': generator,
							    '''num_inference_steps''': 3,
							    '''guidance_scale''': 2.5,
							}
							return inputs






			def      lowerCamelCase    (self       )	->  Optional[int]:


							'''simple docstring'''



							snake_case_		:       Any           =    AudioLDMPipeline.from_pretrained('''cvssp/audioldm'''       )
							snake_case_		:       str           =    audioldm_pipe.to(__magic_name__       )
							audioldm_pipe.set_progress_bar_config(disable=__magic_name__       )

							snake_case_		:       Tuple           =    self.get_inputs(__magic_name__       )
							snake_case_		:       Optional[Any]           =    25
							snake_case_		:       Any           =    audioldm_pipe(**__magic_name__       ).audios[0]

							assert audio.ndim == 1
							assert len(__magic_name__       ) == 8_1920

							snake_case_		:       Union[str, Any]           =    audio[7_7230:7_7240]
							snake_case_		:       str           =    np.array(
							    [-0.4_884, -0.4_607, 0.0_023, 0.5_007, 0.5_896, 0.5_151, 0.3_813, -0.0_208, -0.3_687, -0.4_315]       )
							snake_case_		:       Union[str, Any]           =    np.abs(expected_slice - audio_slice       ).max()
							assert max_diff < 1e-2






			def      lowerCamelCase    (self       )	->  List[str]:


							'''simple docstring'''



							snake_case_		:       Dict           =    AudioLDMPipeline.from_pretrained('''cvssp/audioldm'''       )
							snake_case_		:       List[Any]           =    LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config       )
							snake_case_		:       Dict           =    audioldm_pipe.to(__magic_name__       )
							audioldm_pipe.set_progress_bar_config(disable=__magic_name__       )

							snake_case_		:       List[Any]           =    self.get_inputs(__magic_name__       )
							snake_case_		:       List[Any]           =    audioldm_pipe(**__magic_name__       ).audios[0]

							assert audio.ndim == 1
							assert len(__magic_name__       ) == 8_1920

							snake_case_		:       Any           =    audio[2_7780:2_7790]
							snake_case_		:       List[str]           =    np.array([-0.2_131, -0.0_873, -0.0_124, -0.0_189, 0.0_569, 0.1_373, 0.1_883, 0.2_886, 0.3_297, 0.2_212]       )
							snake_case_		:       List[str]           =    np.abs(expected_slice - audio_slice       ).max()
							assert max_diff < 3e-2


from queue import Queue
from typing import TYPE_CHECKING, Optional


if TYPE_CHECKING:
		from ..models.auto import AutoTokenizer




class 					lowerCamelCase__				:




	'''simple docstring'''




	def   _lowerCamelCase							(   self						:Optional[Any]	,  a						:int		)						->					Any:
								raise NotImplementedError()







	def   _lowerCamelCase							(   self						:Any		)						->					Optional[int]:
								raise NotImplementedError()




class 					lowerCamelCase__				(							__lowercase):




	'''simple docstring'''




	def __init__(   self						:List[Any]	,  a						:"AutoTokenizer"	,  a						:bool = False	,  **a						:Tuple		)						->					Union[str, Any]:
								__UpperCamelCase      :  int       =   tokenizer
								__UpperCamelCase      :  Any       =   skip_prompt
								__UpperCamelCase      :  Union[str, Any]       =   decode_kwargs

								# variables used in the streaming process
								__UpperCamelCase      :  Union[str, Any]       =   []
								__UpperCamelCase      :  str       =   0
								__UpperCamelCase      :  str       =   True







	def   _lowerCamelCase							(   self						:List[str]	,  a						:Union[str, Any]		)						->					Any:
								if len(value.shape		) > 1 and value.shape[0] > 1:
															raise ValueError("TextStreamer only supports batch size 1"		)
								elif len(value.shape		) > 1:
															__UpperCamelCase      :  Optional[int]       =   value[0]

								if self.skip_prompt and self.next_tokens_are_prompt:
															__UpperCamelCase      :  Optional[Any]       =   False
															return

								# Add the new token to the cache and decodes the entire thing.
								self.token_cache.extend(value.tolist()		)
								__UpperCamelCase      :  Optional[int]       =   self.tokenizer.decode(self.token_cache	,  **self.decode_kwargs		)

								# After the symbol for a new line, we flush the cache.
								if text.endswith("\n"		):
															__UpperCamelCase      :  Optional[int]       =   text[self.print_len :]
															__UpperCamelCase      :  Dict       =   []
															__UpperCamelCase      :  int       =   0
								# If the last token is a CJK character, we print the characters.
								elif len(a		) > 0 and self._is_chinese_char(ord(text[-1]		)		):
															__UpperCamelCase      :  Optional[int]       =   text[self.print_len :]
															self.print_len += len(a		)
								# Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words,
								# which may change with the subsequent token -- there are probably smarter ways to do this!)
								else:
															__UpperCamelCase      :  Any       =   text[self.print_len : text.rfind(" "		) + 1]
															self.print_len += len(a		)

								self.on_finalized_text(a		)







	def   _lowerCamelCase							(   self						:List[str]		)						->					List[str]:
								# Flush the cache, if it exists
								if len(self.token_cache		) > 0:
															__UpperCamelCase      :  str       =   self.tokenizer.decode(self.token_cache	,  **self.decode_kwargs		)
															__UpperCamelCase      :  str       =   text[self.print_len :]
															__UpperCamelCase      :  str       =   []
															__UpperCamelCase      :  int       =   0
								else:
															__UpperCamelCase      :  int       =   ""

								__UpperCamelCase      :  Optional[Any]       =   True
								self.on_finalized_text(a	,  stream_end=a		)







	def   _lowerCamelCase							(   self						:Union[str, Any]	,  a						:str	,  a						:bool = False		)						->					str:
								print(a	,  flush=a	,  end="" if not stream_end else None		)







	def   _lowerCamelCase							(   self						:Optional[int]	,  a						:Optional[Any]		)						->					str:
								# This defines a "chinese character" as anything in the CJK Unicode block:
								#   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
								#
								# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
								# despite its name. The modern Korean Hangul alphabet is a different block,
								# as is Japanese Hiragana and Katakana. Those alphabets are used to write
								# space-separated words, so they are not treated specially and handled
								# like the all of the other languages.
								if (
								    (cp >= 0x4e_00 and cp <= 0x9f_ff)
								    or (cp >= 0x34_00 and cp <= 0x4d_bf)  #
								    or (cp >= 0x2_00_00 and cp <= 0x2_a6_df)  #
								    or (cp >= 0x2_a7_00 and cp <= 0x2_b7_3f)  #
								    or (cp >= 0x2_b7_40 and cp <= 0x2_b8_1f)  #
								    or (cp >= 0x2_b8_20 and cp <= 0x2_ce_af)  #
								    or (cp >= 0xf9_00 and cp <= 0xfa_ff)
								    or (cp >= 0x2_f8_00 and cp <= 0x2_fa_1f)  #
								):  #
															return True

								return False




class 					lowerCamelCase__				(							__lowercase):




	'''simple docstring'''




	def __init__(   self						:Any	,  a						:"AutoTokenizer"	,  a						:bool = False	,  a						:Optional[float] = None	,  **a						:Dict		)						->					str:
								super().__init__(a	,  a	,  **a		)
								__UpperCamelCase      :  str       =   Queue()
								__UpperCamelCase      :  Dict       =   None
								__UpperCamelCase      :  Optional[int]       =   timeout







	def   _lowerCamelCase							(   self						:List[Any]	,  a						:str	,  a						:bool = False		)						->					Dict:
								self.text_queue.put(a	,  timeout=self.timeout		)
								if stream_end:
															self.text_queue.put(self.stop_signal	,  timeout=self.timeout		)







	def __iter__(   self						:Tuple		)						->					Tuple:
								return self







	def   _lowerCamelCase							(   self						:List[Any]		)						->					Dict:
								__UpperCamelCase      :  List[Any]       =   self.text_queue.get(timeout=self.timeout		)
								if value == self.stop_signal:
															raise StopIteration()
								else:
															return value